Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.

Existing deep learning systems in the Internet of Things (IoT) environments lack the ability of assigning compute tasks reasonably which leads to resources wasting. In this letter, we propose AAIoT, a method to allocate the inference computation of each network layer to each device in multilayer IoT system. To our best knowledge, this is the first attempt to solve this problem. We design a dynamic programming algorithm to minimize the response time when weighing the cost of computation and transmission. Simulation results show that our approach makes significant improvements in system response time.

Accurate lung segmentation in chest radiographs is a challenging problem due to the presence of strong edges at the rib cage and clavicle, the varying appearance in the upper clavicle bone region, too small costophrenic angle and the lack of a consistent anatomical shape among different individuals. In this paper, we propose a hybrid semi-automatic method called Hull-Closed Polygonal Line Method (Hull-CPLM) to detect the boundaries of the lung Region of Interest (ROI). To the best of our knowledge, this is the first attempt at lung segmentation using the Hull-CPLM in chest radiographs. The proposed method has two main steps: 1) an image preprocessing method is constructed to implement the coarse segmentation by using as low as 15% of the manually delineated points as the initial points, 2) a refinement step is used to fine-tune the segmentation results based on the improved principal curve model and the machine learning model at the refinement step. To prove the performance of the proposed method, both the private and public databases were used. The private database is used to select the optimal parameters for the proposed method, where the result showed a good performance with the Dice Similarity Coefficient (DSC) as high as 97.08%. While on the public databases, our proposed algorithm not only surpassed the performance of different hybrid algorithms but also reached superior segmentation results by comparing with state-of-the-art methods.INDEX TERMS Lung segmentation, chest radiographs, principal curve, closed polygonal line method, database, machine learning.

In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network.

Due to the varying appearance in the upper clavicle bone region, sharp corner at the costophrenic angle, the presence of strong edges at the rib cage and clavicle and the lack of a consistent anatomical shape among different individuals, accurate segmentation of lung on chest radiographs remains challenging. In this work, we propose a novel segmentation method for lung segmentation, containing two subnetworks, where few manually delineated points are used as the approximate initialization. The first one is a preprocessing subnetwork based on a deep learning model (i.e. Deep Belief Network and K-Nearest Neighbor). The second one is a refinement subnetwork, designed to make the preprocessed result to be optimized by combining an improved principal curve method and a machine learning method. To prove the performance of the proposed method, several public datasets were evaluated with Dice Similarity Coefficient (DSC), overlap score (Ω), Sensitivity (Sen), Positive Predictive Value (PPV), global Error (E) and execution time (t). Compared with state-of-the-art methods, our method reaches superior segmentation performance.

Accurate lung segmentation in chest Computed Tomography (CT) scans is a challenging problem because of variations in lung volume shape, susceptibility to partial volume effects that affect thin antero-posterior junction lines, and lack of contrast between the lung and surrounding tissues. To address the need for a robust method for lung segmentation, we present a new method, called Pixel-based two-Scan Connected Component Labeling-Convex Hull-Closed Principal Curve method (PSCCL-CH-CPC), which automatically detects lung boundaries, and surpasses state-of-the-art performance. The proposed method has two main steps: 1) an image preprocessing step to extract coarse lung contours, and 2) a refinement step to refine the coarse segmentation result on the basis of the improved principal curve model and the machine learning model. Experimental results show that the proposed method has good performance, with a Dice Similarity Coefficient (DSC) as high as 98.21%. When compared with state-of-the-art methods, our proposed method achieved superior segmentation results, with an average DSC of 96.9%. INDEX TERMS Automatic lung segmentation, chest CT scans, principal curve, closed principal curve method, machine learning.

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters.

Purpose The purpose of this paper is to reveal the assumptive concept of internationalization because it is discussed and understood in the international business (IB) literature. This paper develops a framework to reconceptualize internationalization in the context of global value chain (GVC) and sustainability. Based on this conceptual framework, this paper aims to formulate interrelated propositions to define internationalization. Design/methodology/approach This paper reviews the assumptive conceptualization process based on empirical evidence obtained through qualitative, illustrative and descriptive content analysis methods. Through the collection and qualitative content analysis of milestone papers, this paper demonstrates the fragmentation of the concept of internationalization. This paper reviews the evolving nature of the concept of internationalization, analyzing the accumulative issues associated with defining internationalization, as well as its potential future development. Findings This paper introduces a dynamic perspective on the evolving nature of the concept of internationalization and argue there is a need to reconceptualize internationalization in the context of the GVC and sustainability. Originality/value After reviewing the context in which the term “internationalization” has been applied and taking into consideration the current trends in the IB, this paper formulates an updated definition of the term internationalization. This paper offers a viewpoint on the future direction of the concept of internationalization in light of the growing importance of sustainability within IB.